Elements of information theory
Elements of information theory
A maximum entropy approach to natural language processing
Computational Linguistics
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Inducing Features of Random Fields
Inducing Features of Random Fields
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Towards history-based grammars: using richer models for probabilistic parsing
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Learning dependencies between case frame slots
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Semantic classes and syntactic ambiguity
HLT '93 Proceedings of the workshop on Human Language Technology
Generalizing case frames using a thesaurus and the MDL principle
Computational Linguistics
General-to-specific model selection for subcategorization preference
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Zero-anaphora resolution by learning rich syntactic pattern features
ACM Transactions on Asian Language Information Processing (TALIP)
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This paper proposes a novel method of learning probabilistic subcategorization preference. In the method, for the purpose of coping with the ambiguities of case dependencies and noun class generalization of argument/adjunct nouns, we introduce a data structure which represents a tuple of independent partial subcategorization frames. Each collocation of a verb and argument/adjunct nouns is assumed to be generated from one of the possible tuples of independent partial subcategorization frames. Parameters of subcategorization preference are then estimated so as to maximize the subcategorization preference function for each collocation of a verb and argument/adjunct nouns in the training corpus. We also describe the results of the experiments on learning probabilistic subcategorization preference from the EDR Japanese bracketed corpus, as well as those on evaluating the performance of subcategorization preference.